Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities

Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation, with an intraclass correlation coefficient (ICC) of 0.90 (95% confidence interval [CI], 0.80-0.95) for WMH volume and median similarity index (SI) of 0.58 (interquartile range [IQR] 0.50-0.65). The method also showed similarly high levels of agreement with semi-automated segmentation, with ICC 0.92 (95% CI 0.89-0.93) and median SI 0.56 (IQR 0.49-0.66). Its best performance was observed for the highest tertile of WMH volume. Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study.